TY - GEN
T1 - Learning feature weights from positive cases
AU - Gunawardena, Sidath
AU - Weber, Rosina O.
AU - Stoyanovich, Julia
PY - 2013
Y1 - 2013
N2 - The availability of new data sources presents both opportunities and challenges for the use of Case-based Reasoning to solve novel problems. In this paper, we describe the research challenges we faced when trying to reuse experiences of successful academic collaborations available online in descriptions of funded grant proposals. The goal is to recommend the characteristics of two collaborators to complement an academic seeking a multidisciplinary team; the three form a collaboration that resembles a configuration that has been successful in securing funding. While seeking a suitable measure for computing similarity between cases, we were confronted with two challenges: a problem context with insufficient domain knowledge and data that consists exclusively of successful collaborations, that is, it contains only positive instances. We present our strategy to overcome these challenges, which is a clustering-based approach to learn feature weights. Our approach identifies poorly aligned cases, i.e., ones that violate the assumption that similar problems have similar solutions. We use the poorly aligned cases as negatives in a feedback algorithm to learn feature weights. The result of this work is an integration of methods that makes CBR useful to yet another context and in conditions it has not been used before.
AB - The availability of new data sources presents both opportunities and challenges for the use of Case-based Reasoning to solve novel problems. In this paper, we describe the research challenges we faced when trying to reuse experiences of successful academic collaborations available online in descriptions of funded grant proposals. The goal is to recommend the characteristics of two collaborators to complement an academic seeking a multidisciplinary team; the three form a collaboration that resembles a configuration that has been successful in securing funding. While seeking a suitable measure for computing similarity between cases, we were confronted with two challenges: a problem context with insufficient domain knowledge and data that consists exclusively of successful collaborations, that is, it contains only positive instances. We present our strategy to overcome these challenges, which is a clustering-based approach to learn feature weights. Our approach identifies poorly aligned cases, i.e., ones that violate the assumption that similar problems have similar solutions. We use the poorly aligned cases as negatives in a feedback algorithm to learn feature weights. The result of this work is an integration of methods that makes CBR useful to yet another context and in conditions it has not been used before.
KW - Case Alignment
KW - Case Cohesion
KW - Density Clustering
KW - Multidisciplinary Collaboration
KW - Recommender Systems
KW - Single Class Learning
KW - Subspace Clustering
UR - http://www.scopus.com/inward/record.url?scp=84884520840&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84884520840&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-39056-2_10
DO - 10.1007/978-3-642-39056-2_10
M3 - Conference contribution
AN - SCOPUS:84884520840
SN - 9783642390555
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 134
EP - 148
BT - Case-Based Reasoning Research and Development - 21st International Conference, ICCBR 2013, Proceedings
T2 - 21st International Conference on Case-Based Reasoning Research and Development, ICCBR 2013
Y2 - 8 July 2013 through 11 July 2013
ER -